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Attention Deficit Hyperactivity Disorder Detection Using Deep Learning Approach

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dc.contributor.author Hamim, Md Abrar
dc.contributor.author Tanmoy, F.M.
dc.contributor.author Tasfia, Orin
dc.contributor.author Juthi, Farzana Alam
dc.date.accessioned 2024-04-28T10:10:26Z
dc.date.available 2024-04-28T10:10:26Z
dc.date.issued 2023-11-23
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12192
dc.description.abstract ADHD, a neurodevelopmental disorder characterized by hyperactivity, inattention, and impulsivity, has many detrimental impacts and is out of proportion to age. ADHD causes executive failure and emotional instability, which can lower academic performance. We propose a machine learning and artificial intelligence-driven approach to diagnose and early detect this disease and assist ADHD medicine. SVM, logistic regression, XGBoost, AdaBoost, and two deep learning models were applied to our dataset (ANN and CNN). Our ANN model had 99% accuracy in dependability, expandability, and generalizability. We plan to use our machine learning technology to enhance ADHD diagnosis and treatment for everyone. en_US
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Deep learning en_US
dc.subject Deficit hyperactivity en_US
dc.subject Disorder detection en_US
dc.title Attention Deficit Hyperactivity Disorder Detection Using Deep Learning Approach en_US
dc.type Article en_US


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